Water treatment plant and method for operating water treatment plant

ABSTRACT

A water treatment plant which performs water treatment using a water treatment device includes an imaging device, a processing device, and a control device. The imaging device images a water treatment environment of the water treatment device and outputs image data obtained by imaging. The processing device causes an arithmetic device which performs an arithmetic operation using one or more calculation models generated by machine learning to execute the arithmetic operation employing the image data output from the imaging device as input data of the one or more calculation models. The control device controls the water treatment device on the basis of output information output from the arithmetic device by executing the arithmetic operation.

FIELD

The present invention relates to a water treatment plant which performstreatment of water such as clean water or sewage and a method foroperating the water treatment plant.

BACKGROUND

In a water treatment plant, water treatment control is performed whilechanging a control target value depending on environmental changes. Forexample, by changing the control target value along with changes in awater treatment environment such as seasonal temperature difference, theflow rate of inflow water, and the quality of inflow water, watertreatment control depending on changes in the water treatmentenvironment is performed in the water treatment plant.

The control target value is changed by an operator on the basis of pastexperience and the like, and specialized expertise is required forperforming the change. Patent Literature 1 proposes a technique whichuses an artificial intelligent (AI) device for controlling a sewagetreatment device so that experience of an operator can be reflected inchanging a control target value depending on environmental changes. Insuch a technique, detection values of multiple sensors which detect theflow rate, temperature, biochemical oxygen demand (BOD), NH₄+, and thelike of inflow water to the sewage treatment device are input to the AIdevice, and the sewage treatment device is controlled on the basis of anoutput of the AI device.

CITATION LIST Patent Literature

Patent Literature 1: Japanese Patent Application Laid-open No.2004-25160

SUMMARY Technical Problem

In such a conventional water treatment plant described above, watertreatment control using an AI device is performed with the use ofnumerical values of the flow rate, temperature, BOD, NH₄+ and the likeof inflow water as indices. However, such a conventional water treatmentplant described above has room for improvement. For example, there maybe a case where effective water treatment control cannot be performed insuch a conventional water treatment plant described above with respectto a change in a water treatment environment of a water treatmentdevice, the change not appearing in a numerical value detected by asensor.

The present invention has been made in view of the above, and an objectthereof is to obtain a water treatment plant capable of performing moreeffective water treatment control with respect to a change in a watertreatment environment.

Solution to Problem

A water treatment plant according to the present invention is a watertreatment plant which performs water treatment using a water treatmentdevice, and includes an imaging device, a processing device, and acontrol device. The imaging device images a water treatment environmentof the water treatment device and outputs image data obtained byimaging. The processing device causes an arithmetic device whichperforms an arithmetic operation using one or more calculation modelsgenerated by machine learning to execute the arithmetic operationemploying the image data output from the imaging device as input data ofthe one or more calculation models. The control device controls thewater treatment device on the basis of information output from thearithmetic device by executing the arithmetic operation.

Advantageous Effects of Invention

The present invention achieves an effect that it is possible to providea water treatment plant capable of performing more effective watertreatment control with respect to a change in a water treatmentenvironment.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating an outline of a water treatment plantaccording to a first embodiment.

FIG. 2 is a diagram illustrating an example configuration of the watertreatment plant according to the first embodiment.

FIG. 3 is a diagram illustrating example configurations of multiplesensor groups according to the first embodiment.

FIG. 4 is a diagram illustrating an example configuration of aprocessing device according to the first embodiment.

FIG. 5 is a diagram illustrating an example of a data table stored in astorage device according to the first embodiment.

FIG. 6 is a diagram illustrating an example configuration of anarithmetic device according to the first embodiment.

FIG. 7 is a diagram illustrating an example configuration of a controldevice according to the first embodiment.

FIG. 8 is a flowchart illustrating an example of a series of processesof the processing device according to the first embodiment.

FIG. 9 is a flowchart illustrating an example of a series of processesof the arithmetic device according to the first embodiment.

FIG. 10 is a flowchart illustrating an example of a series of processesof the control device according to the first embodiment.

FIG. 11 is a diagram illustrating an example of a hardware configurationof the processing device according to the first embodiment.

DESCRIPTION OF EMBODIMENTS

Hereinafter, a water treatment plant and a method for operating thewater treatment plant according to an embodiment of the presentinvention will be described in detail with reference to the drawings.The present invention is not limited to the embodiment.

First Embodiment

FIG. 1 is a diagram illustrating an outline of a water treatment plantaccording to a first embodiment. As illustrated in FIG. 1, a watertreatment plant 1 according to the first embodiment includes a watertreatment device 10, an imaging device 20, a processing device 30, anarithmetic device 40, and a control device 50. The arithmetic device 40is an example of an AI device.

The water treatment device 10 is, for example, a device which performstreatment of water such as clean water or sewage, and includes a deviceto be controlled such as a pump or a blower which controls a watertreatment state. An example of the water treatment device 10 is notlimited to the device according to the first embodiment which includesthe device to be controlled such as a pump or a blower, and a gritchamber, a primary settling basin, a sludge-reducing device, and thelike of the water treatment plant may be used.

The control device 50 controls the water treatment device 10. Theimaging device 20 images a water treatment environment of the watertreatment device 10 and outputs image data of the water treatmentenvironment obtained by imaging. The water treatment environment of thewater treatment device 10 includes at least one of a water treatmentenvironment inside the water treatment device 10 and a water treatmentenvironment outside the water treatment device 10. The processing device30 acquires image data from the imaging device 20.

The processing device 30 causes the arithmetic device 40 to execute anarithmetic operation employing the acquired image data as input data,and acquires a result of the arithmetic operation by the arithmeticdevice 40 from the arithmetic device 40. The arithmetic device 40includes a calculation model generated by machine learning. Such acalculation model receives an input of the image data of the imagingdevice 20, and outputs information on a control target value of thedevice to be controlled, for example. The control target value is, forexample, a target value of the amount of control of the device to becontrolled such as a pump or a blower which controls a water treatmentstate of the water treatment device 10.

The arithmetic device 40 performs an arithmetic operation using theabove-described calculation model and employing the image data acquiredfrom the processing device 30 as input data, and outputs informationincluding the result of the arithmetic operation by the arithmeticdevice 40 to the processing device 30. The processing device 30 outputsthe information acquired from the arithmetic device 40 to the controldevice 50. The control device 50 controls the water treatment device 10on the basis of the information output from the processing device 30.For example, in a case where the information output from the arithmeticdevice 40 is the information on the control target value of the deviceto be controlled, the control device 50 can control the water treatmentdevice 10 by outputting control information including the control targetvalue to the device to be controlled of the water treatment device 10.The arithmetic device 40 is, for example, AI called artificialintelligence or the like, and contributes to estimation of a preferablecontrol target value of the device to be controlled through machinelearning based on the input image data.

As described above, in the water treatment plant 1, the water treatmentcontrol can be performed using the arithmetic device 40 and employing animage of the water treatment environment of the water treatment device10 as a new index. Therefore, in the water treatment plant 1, it ispossible to perform, with the use of the arithmetic device 40, forexample, water treatment control which has been performed by an operatorof the water treatment plant 1 on the basis of the image of the watertreatment environment of the water treatment device 10 and on the basisof past experience or knowledge of the operator, and to performeffective water treatment control.

In the above-described first embodiment, the example has been describedin which the image data of the imaging device 20 is output to thearithmetic device 40 via the processing device 30, and the result of thearithmetic operation by the arithmetic device 40 is output to theprocessing device 30, thereby controlling the control device 50.However, the present invention is not limited to the example. Forexample, modification may be made so that a function of the processingdevice 30 is incorporated into at least one of the arithmetic device 40and the control device 50 to omit the processing device 30. In thismodification, for example, the processing device 30 which is separatefrom at least one of the arithmetic device 40 and the control device 50can be omitted, so that an effect of increasing the degree of freedom indevice configuration is achieved.

Hereinafter, the water treatment plant 1 according to the firstembodiment will be described in detail. FIG. 2 is a diagram illustratingan example configuration of the water treatment plant according to thefirst embodiment. In the following, sewage treatment will be describedas an example of water treatment performed by the water treatment device10.

As illustrated in FIG. 2, the water treatment plant 1 according to thefirst embodiment includes the above-described water treatment device 10,imaging devices 20 ₁ to 20 ₃, sensor groups 21 ₁ to 21 ₃, the processingdevice 30, the arithmetic device 40, the control device 50, a storagedevice 61, a display device 62, and an input device 63. In the followingdescription, the imaging devices 20 ₁ to 20 ₃ may be referred to as theimaging device 20 when they are indicated without being distinguishedfrom each other, and the sensor groups 21 ₁ to 21 ₃ may be referred toas the sensor group 21 when they are indicated without beingdistinguished from each other.

The processing device 30, the arithmetic device 40, the control device50, the storage device 61, the display device 62, and the input device63 are communicably connected to each other via a communication network64. The communication network 64 is, for example, a local area network(LAN), a wide area network (WAN), a bus, or a leased line.

The water treatment device 10 illustrated in FIG. 2 is a sewagetreatment device which treats sewage. Such a water treatment device 10includes a primary settling tank 11 which stores sewage as inflow waterfrom sewerage and the like, and settles a solid substance and the likein the sewage, the solid substance being relatively easy to sink, atreatment tank 12 which aerobically treats supernatant water in theprimary settling tank 11, and a final settling tank 13 which separates aliquid mixture containing activated sludge flowing from the treatmenttank 12 into supernatant water and activated sludge. The supernatantwater in the final settling tank 13 is discharged as treated water fromthe final settling tank 13.

In the treatment tank 12, the supernatant water which flows therein fromthe primary settling tank 11 contains organic matter, and the organicmatter contained in the supernatant water is treated, for example, bydigestion by aerobic microorganisms such as phosphorus accumulatingbacteria, nitrifying bacteria, and denitrifying bacteria.

The water treatment device 10 further includes a blower 14 which blowsair into the treatment tank 12 to dissolve the air in the liquid mixturecontaining activated sludge, and a pump 15 which is provided on a pipewhich connects the final settling tank 13 and the treatment tank 12, andreturns the activated sludge to the treatment tank 12 from the finalsettling tank 13. Each of the blower 14 and the pump 15 is an example ofthe device to be controlled described above, and hereinafter, the blower14 and the pump 15 may be referred to as the device to be controlledwhen they are indicated without being distinguished from each other.

The multiple imaging devices 20 ₁, 20 ₂, and 20 ₃ image water treatmentenvironments of the water treatment device 10 which are objects to beimaged different from each other. The imaging device 20 ₁ images a watertreatment environment which is an object to be imaged inside the primarysettling tank 11. The object to be imaged inside the primary settlingtank 11 is, for example, a state of water, a state of bubbles, or astate of settlings in the primary settling tank 11.

The imaging device 20 ₂ images a water treatment environment which is anobject to be imaged inside the treatment tank 12. The object to beimaged inside the treatment tank 12 is, for example, a state ofactivated sludge or a state of water in the treatment tank 12. The stateof activated sludge includes, for example, the amount or distribution ofthe activated sludge. The state of activated sludge may be, for example,the amount of each microorganism.

The imaging device 20 ₃ images a water treatment environment which is anobject to be imaged inside the final settling tank 13. The object to beimaged inside the final settling tank 13 is, for example, a state ofsupernatant water or a state of settlings in the final settling tank 13.In the following description, the primary settling tank 11, thetreatment tank 12, and the final settling tank 13 may be referred to asa tank when they are indicated without being distinguished from eachother. The objects to be imaged which are imaged by the imaging device20 are not limited to the above-described examples, and the imagingdevice 20 can also image a state of an inner wall of the tank, a stateof surroundings of the tank, or the like as the object to be imaged.Although the imaging devices 20 ₁, 20 ₂, and 20 ₃ illustrated in FIG. 2image the state or environment inside the water treatment device 10 asthe water treatment environment of the water treatment device 10, animaging device may be provided which images a state or environmentoutside the water treatment device 10 illustrated in FIG. 2.

The imaging device 20 is, for example, a digital camera, or a digitalmicroscope. The imaging device 20 may be, for example, a digital camerafor a microscope. In such a case, when the operator of the watertreatment plant 1 places water in the tank or the like under themicroscope, the imaging device 20 can image a microscopic image thereof.The number of imaging devices 20 is not limited to three, and may be twoor less, or four or more. Hereinafter, the operator of the watertreatment plant 1 will be simply referred to as the operator.

The multiple sensor groups 21 ₁ to 21 ₃ detect various characteristicsindicating the water treatment environment of the water treatment device10. For example, the sensor group 21 ₁ detects an inflow watercharacteristic which is a characteristic of inflow water to the primarysettling tank 11. The sensor group 21 ₂ detects an intra-treatment-tankcharacteristic which indicates a state of the treatment tank 12. Thesensor group 21 ₃ detects a treated water characteristic which is acharacteristic of treated water discharged from the final settling tank13.

FIG. 3 is a diagram illustrating example configurations of the multiplesensor groups according to the first embodiment. As illustrated in FIG.3, the sensor group 21 ₁ includes a flow rate sensor 22 ₁ which detectsthe inflow amount of inflow water, a BOD sensor 22 ₂ which detects theBOD of the inflow water, a water temperature sensor 22 ₃ which detectsthe temperature of the inflow water, and an NH₃ sensor 22 ₄ whichdetects the NH₃ concentration in the inflow water. The sensor group 21 ₁may include a sensor for detecting the NH₄ ⁺ or ammoniacal nitrogenconcentration in the inflow water instead of or in addition to the NH₃sensor 22 ₄.

The sensor group 21 ₂ includes a dissolved oxygen sensor 23 ₁ whichdetects the amount of dissolved oxygen in the treatment tank 12, anactive microorganism concentration sensor 23 ₂ which detects the activemicroorganisms concentration in the treatment tank 12, and a BOD sensor23 ₃ which detects the BOD in the treatment tank 12. In addition, thesensor group 21 ₂ further includes multiple sensors each of whichdetects one of the ammoniacal nitrogen concentration, a nitrate nitrogenconcentration, a total nitrogen concentration, a phosphate phosphorusconcentration, and a total phosphorus concentration.

The sensor group 21 ₃ includes a flow rate sensor 24 ₁ which detects theoutflow amount of treated water, a BOD sensor 24 ₂ which detects the BODof the treated water, and a total nitrogen concentration sensor 24 ₃which detects the total nitrogen concentration in the treated water.

The sensor groups 21 ₁ to 21 ₃ may include a sensor which detects anobject other than the above-described objects to be detected, or may notinclude a part of the multiple sensors described above. Hereinafter,data of numerical values detected by each sensor in the sensor groups 21₁ to 21 ₃ will be referred to as numerical data. In addition, image dataand numerical data may be referred to as detection data when they areindicated without being distinguished from each other.

The processing device 30 acquires image data output from the imagingdevice 20 and numerical data output from the sensor group 21, and storesthe acquired image data and numerical data in the storage device 61. Theprocessing device 30 causes the arithmetic device 40 to execute anarithmetic operation employing data selected between the image dataoutput from the imaging device 20 and the numerical data output from thesensor group 21 as input data, and acquires information including aresult of the arithmetic operation by the arithmetic device 40. Theprocessing device 30 transmits the information output from thearithmetic device 40 to the control device 50, and stores theinformation output from the arithmetic device 40 in the storage device61.

In addition, the processing device 30 can display the image data outputfrom the imaging device 20 on the display device 62. The operator candetermine, for example, on the basis of an image of the inside of thetank displayed on the display device 62, whether there is a sign of afuture unfavorable intra-tank state in the water treatment device 10.The term “future” here means, for example, several hours ahead or one ormore days ahead.

The future unfavorable intra-tank state includes, for example, a statewhere the removal of organic matter becomes insufficient, a state wherethe removal of nitrogen becomes insufficient, and a state where afiltration membrane (not illustrated) becomes easily clogged. Inaddition, the sign of the future unfavorable intra-tank state includes,for example, a state where the number of microorganisms which inhibitwater treatment is increasing, or a state where the distribution ofmicroorganisms which perform water treatment exhibits a specificdistribution. In the water treatment plant 1 according to the firstembodiment, it is possible to determine the sign of the futureunfavorable intra-tank state described above on the basis of the imagedata of the imaging device 20. Therefore, it is possible to contributeto improvement in diversification and accuracy of grounds fordetermination of signs, as compared with determination of signs usingnumerical data only. Hereinafter, the sign of the future unfavorableintra-tank state may be simply referred to as the sign.

In a case where the operator determines that the image of the inside ofthe tank displayed on the display device 62 indicates theabove-described sign, by operating the input device 63, the operator cangenerate or update a calculation model included in the arithmetic device40 employing, as learning data, image data at a time when anenvironmental change indicating the sign occurs.

FIG. 4 is a diagram illustrating an example configuration of theprocessing device according to the first embodiment. As illustrated inFIG. 4, the processing device 30 includes a communication unit 31, astorage unit 32, and a control unit 33. The communication unit 31 isconnected to the communication network 64. The control unit 33 cantransmit and receive data to and from each of the arithmetic device 40,the control device 50, the storage device 61, the display device 62, andthe input device 63 via the communication unit 31 and the communicationnetwork 64.

The control unit 33 includes a data processing unit 34, a displayprocessing unit 35, an arithmetic-operation request unit 36, anacceptance processing unit 37, and a switching unit 38. The dataprocessing unit 34 repeatedly acquires image data output from theimaging device 20 and numerical data output from the sensor group 21,and stores the acquired image data and numerical data in the storagedevice 61.

The data processing unit 34 stores the image data acquired from eachimaging device 20 in the storage device 61 in association with time. Inaddition, the data processing unit 34 stores the numerical data acquiredfrom each sensor in the storage device 61 in association with time.Furthermore, the data processing unit 34 acquires information outputfrom the arithmetic device 40, outputs the acquired information to thecontrol device 50, and stores the acquired information in the storagedevice 61.

FIG. 5 is a diagram illustrating an example of a data table stored inthe storage device according to the first embodiment. The data tableillustrated in FIG. 5 includes image data, numerical data, and controltarget values for each time. In FIG. 5, image data IM1(t 0), IM1(t 1), .. . , IM1(tm), . . . , and IM1(tn) are image data of the imaging device20 ₁. In addition, image data IM2(t 0), IM2(t 1), . . . , IM2(tm), . . ., and IM2(tn) are image data of the imaging device 20 ₂.

Furthermore, image data IM3(t 0), IM3(t 1), . . . , IM3(tm), . . . , andIM3(tn) are image data of the imaging device 20 ₃. Note that m and n arenatural numbers, and n>m is established. Although FIG. 5 illustratesnumerical data of one sensor, i.e. NU1(t 0), NU1(t 1), . . . , NU1(tm),. . . , and NU1(tn) only, the data table also includes numerical data ofthe rest of sensors.

In addition, the data table illustrated in FIG. 5 includes informationon the control target value of each device to be controlled output tothe control device 50 by the processing device 30 at each time. In FIG.5, control target values RV1(t 0), RV1(t 1), . . . , RV1(tm), . . . ,and RV1(tn) are control target values of the blower 14. In addition,control target values RV2(t 0), RV2(t 1), . . . , RV2(tm), . . . , andRV2(tn) are control target values of the pump 15.

Returning to FIG. 4, the description of the control unit 33 will becontinued. The display processing unit 35 displays the image data andthe numerical data acquired by the data processing unit 34 on thedisplay device 62. In addition, the display processing unit 35 canacquire, from the storage device 61, the information input by theoperator operating the input device 63, and can display the acquiredinformation on the display device 62.

The arithmetic-operation request unit 36 outputs, to the arithmeticdevice 40 via the communication network 64, data necessary for inputtinga calculation model which satisfies a selection condition describedlater, out of the image data and the numerical data acquired by the dataprocessing unit 34.

For example, in a case where the calculation model which satisfies theselection condition is a calculation model for an image, thearithmetic-operation request unit 36 outputs the image data acquired bythe data processing unit 34 to the arithmetic device 40. In addition, ina case where the calculation model which satisfies the selectioncondition is a calculation model for a sensor, the arithmetic-operationrequest unit 36 outputs the numerical data acquired by the dataprocessing unit 34 to the arithmetic device 40.

Furthermore, in a case where the calculation models which satisfy theselection conditions are the calculation model for an image and thecalculation model for a sensor, the arithmetic-operation request unit 36outputs the image data and the numerical data acquired by the dataprocessing unit 34 to the arithmetic device 40. It is also possible forthe arithmetic-operation request unit 36 to acquire the data necessaryfor inputting the calculation model which satisfies the selectioncondition from the storage device 61 and to output the acquired data tothe arithmetic device 40.

The arithmetic-operation request unit 36 outputs detection data to thearithmetic device 40, thereby causing the arithmetic device 40 toexecute an arithmetic operation employing the detection data as inputdata. The data processing unit 34 acquires information indicating aresult of the arithmetic operation output from the arithmetic device 40,and outputs the acquired information to the control device 50. Theinformation output from the arithmetic device 40 includes, for example,control information including the control target value of the device tobe controlled, and the control device 50 controls the water treatmentdevice 10 by controlling the device to be controlled provided in thewater treatment device 10 on the basis of the information output fromthe processing device 30.

The acceptance processing unit 37 accepts selection of image data forgenerating and updating multiple calculation models included in thearithmetic device 40 on the basis of an operation on the input device 63performed by the operator. The arithmetic-operation request unit 36acquires image data, the selection of which has been accepted by theacceptance processing unit 37, from the storage device 61. In addition,the arithmetic-operation request unit 36 acquires, from the storagedevice 61, information on the control target value of each device to becontrolled associated with time when the selected image data wasacquired.

The arithmetic-operation request unit 36 transmits learning data inwhich the selected image data and object-to-be-controlled data areassociated with each other, to the arithmetic device 40 via thecommunication network 64. In the learning data, theobject-to-be-controlled data associated with the selected image data isdata including the control target values acquired from the storagedevice 61 and the type of each device to be controlled. For example, ina case where the selected image data are image data IM1(tm), IM2(tm),and IM3(tm) at a time tm illustrated in FIG. 5, theobject-to-be-controlled data includes control target values RV1(tm) andRV2(tm) illustrated in FIG. 5.

The acceptance processing unit 37 can also accept information on aperiod for selecting time-series image data stored in the storage device61, on the basis of the operation on the input device 63 performed bythe operator. For example, the acceptance processing unit 37 can acceptan operation on the input device 63 for selecting image data for thepast year.

The arithmetic-operation request unit 36 acquires, from the storagedevice 61, time-series image data output from the imaging device 20during the period accepted by the acceptance processing unit 37. Inaddition, the arithmetic-operation request unit 36 acquires, from thestorage device 61, data of time-series control target values set in eachdevice to be controlled during the period accepted by the acceptanceprocessing unit 37. The arithmetic-operation request unit 36 transmitslearning data including the acquired time-series image data and data oftime-series control target values to the arithmetic device 40 via thecommunication network 64.

In addition, as will be described later, in a case where the calculationmodel for an image is, for example, a recurrent neural network whichoutputs information on a score indicating the degree whether anenvironmental change indicating the above-described sign has occurred,the operator can select correct data and incorrect data. For example,the operator can select, as the correct data, image data imaged by theimaging device 20 in a state where there is the above-described sign inthe water treatment device 10. In addition, the operator can select, asthe incorrect data, for example, image data imaged by the imaging device20 at a time when there is no above-described sign.

The switching unit 38 can operate in a manual switching mode in whichthe selection condition is changed on the basis of the operation on theinput device 63 performed by the operator. For example, in a case wherethe acceptance processing unit 37 accepts a selection conditionswitching operation performed by the operator when the operation mode ofthe switching unit 38 is the manual switching mode, the switching unit38 changes the selection condition set in the storage unit 32.

In addition, the switching unit 38 can also operate in an automaticswitching mode in which the selection condition is automaticallychanged. For example, in a case where the operation mode of theswitching unit 38 is the automatic switching mode and the selectioncondition is set at the calculation model for a sensor, the switchingunit 38 determines whether a first switching condition is satisfied. Ifit is determined that the first switching condition is satisfied, theswitching unit 38 changes the selection condition set in the storageunit 32 from the calculation model for a sensor to the calculation modelfor an image. As a result, the calculation model used in the arithmeticdevice 40 is changed to the calculation model for an image.

For example, in a case where a numerical value indicated by numericaldata of one or more specific sensors included in the multiple sensorgroups 21 is outside a preset range continuously for a preset period oftime or longer, the switching unit 38 can determine that the firstswitching condition is satisfied. The first switching condition is notlimited to conditions of the detection results of the sensors, and maybe a condition of, for example, time of day, season, weather, or anyother condition.

In addition, in a case where the operation mode of the switching unit 38is the automatic switching mode and the calculation model for an imageis set as the selection condition, the switching unit 38 determineswhether a second switching condition is satisfied. If it is determinedthat the second switching condition is satisfied, the switching unit 38changes the selection condition set in the storage unit 32 from thecalculation model for an image to the calculation model for a sensor. Asa result, the calculation model used in the arithmetic device 40 ischanged to the calculation model for a sensor.

For example, in a case where a numerical value indicated by numericaldata of one or more specific sensors included in the multiple sensorgroups 21 is inside a preset range continuously for a preset period oftime or longer, the switching unit 38 can determine that the secondswitching condition is satisfied. The second switching condition is notlimited to conditions of the detection results of the sensors, and maybe a condition of, for example, time of day, season, weather, or anyother condition.

The operation mode of the switching unit 38 can be changed on the basisof an operation performed by the operator. In addition, the switchingunit 38 can change the calculation models alternately between thecalculation model for a sensor and the calculation model for an image.For example, the switching unit 38 can set the calculation model for asensor in a first period T1, and can set the calculation model for animage in a second period T2 which comes alternately with the firstperiod T1. In such a case, it is possible to mainly perform watertreatment control with numerical values while performing water treatmentcontrol with images by making the second period T2 shorter than thefirst period T1.

Next, the arithmetic device 40 will be described. FIG. 6 is a diagramillustrating an example configuration of the arithmetic device accordingto the first embodiment. As illustrated in FIG. 6, the arithmetic device40 includes a communication unit 41, a storage unit 42, and a controlunit 43.

The communication unit 41 is connected to the communication network 64.The control unit 43 can transmit and receive data to and from each ofthe imaging device 20, the processing device 30, the control device 50,the storage device 61, and the input device 63 via the communicationunit 41 and the communication network 64.

The storage unit 42 stores multiple calculation models. The multiplecalculation models stored in the storage unit 42 include theabove-described calculation model for an image and calculation model fora sensor.

The calculation model for an image is, for example, a convolutionalneural network which receives inputs of multiple image data output frommultiple imaging devices 20 and outputs control target values ofmultiple devices to be controlled. With the use of the convolutionalneural network, as compared to a case of using a general neural network,learning of image data is efficiently performed by sharing weights,which makes it possible to acquire highly accurate results. Inconsideration of the variety of system architecture, the calculationmodel for an image may be a neural network other than the convolutionalneural network.

The calculation model for a sensor is, for example, a neural networkwhich receives inputs of multiple numerical data output from multiplesensors provided in the multiple sensor groups 21 ₁ to 21 ₃ and outputscontrol target values of multiple devices to be controlled. Thecalculation model for a sensor is a neural network suitable for anarithmetic operation of numerical data, unlike the convolutional neuralnetwork which is a calculation model for an image. In addition, forexample, the calculation model for a sensor may be a calculation modelgenerated by a learning algorithm such as linear regression or logisticregression. The calculation model for a sensor may be a convolutionalneural network because the degree of freedom in device configuration isincreased.

The control unit 43 includes an acquisition processing unit 44, anarithmetic processing unit 45, an output processing unit 46, and alearning processing unit 47. The acquisition processing unit 44 acquiresdetection data from the processing device 30 via the communicationnetwork 64 and the communication unit 41. The detection data from theprocessing device 30 includes image data, numerical data, or image dataand numerical data, as described above.

The arithmetic processing unit 45 reads, from the storage unit 42, acalculation model corresponding to the detection data acquired by theacquisition processing unit 44, inputs the detection data to the readcalculation model to perform an arithmetic operation using thecalculation model, thereby acquiring an output of the calculation model.For example, in a case where the detection data acquired by theacquisition processing unit 44 is image data, the arithmetic processingunit 45 inputs the image data to the calculation model for an image toperform an arithmetic operation using the calculation model for animage, and acquires an output of the calculation model for an image.

In addition, in a case where the detection data acquired by theacquisition processing unit 44 is numerical data, the arithmeticprocessing unit 45 inputs the numerical data to the calculation modelfor a sensor to perform an arithmetic operation using the calculationmodel for a sensor, and acquires an output of the calculation model fora sensor.

In addition, in a case where the detection data acquired by theacquisition processing unit 44 includes image data and numerical data,the arithmetic processing unit 45 uses both the calculation model for animage and the calculation model for a sensor. That is, the arithmeticprocessing unit 45 inputs the image data out of the image data and thenumerical data to the calculation model for an image to perform anarithmetic operation using the calculation model for an image, andacquires information output from the calculation model for an image.Furthermore, the arithmetic processing unit 45 inputs the numerical dataout of the image data and the numerical data to the calculation modelfor a sensor to perform an arithmetic operation using the calculationmodel for a sensor, and acquires information output from the calculationmodel for a sensor.

The output processing unit 46 outputs, as the output information of thearithmetic device 40, information acquired by the arithmetic operationusing each calculation model in the arithmetic processing unit 45 to theprocessing device 30 from the communication unit 41. The informationoutput from each calculation model is information on the control targetvalues of the multiple devices to be controlled described above.

In the case where the detection data acquired by the acquisitionprocessing unit 44 includes image data and numerical data, the outputprocessing unit 46 can select one of information output from thecalculation model for a sensor and information output from thecalculation model for an image to output the selected information to theprocessing device 30 from the communication unit 41.

For example, in a case where a difference between the control targetvalue output from the calculation model for an image and the controltarget value output from the calculation model for a sensor is a presetvalue or larger, the output processing unit 46 selects the controltarget value output from the calculation model for an image and outputsthe control target value to the processing device 30. In addition, in acase where the difference between the control target value output fromthe calculation model for an image and the control target value outputfrom the calculation model for a sensor is smaller than the presetvalue, the output processing unit 46 selects the control target valueoutput from the calculation model for a sensor and outputs the controltarget value to the processing device 30.

In the case where the detection data acquired by the acquisitionprocessing unit 44 includes image data and numerical data, thearithmetic processing unit 45 can perform, for each device to becontrolled, an arithmetic operation of an average value of the controltarget value output from the calculation model for a sensor and thecontrol target value output from the calculation model for an image. Theoutput processing unit 46 can output, as output information, controlinformation including the average value of the control target values foreach device to be controlled obtained by the arithmetic operation by thearithmetic processing unit 45.

The calculation model for an image may include a recurrent neuralnetwork in addition to the convolutional neural network described above.In such a case, the arithmetic processing unit 45 inputs time-seriesimage data imaged by the imaging device 20 to the recurrent neuralnetwork, and acquires, from the recurrent neural network, data of animage predicted to be imaged by the imaging device 20 after the elapseof a time Ta. The time Ta is, for example, 12 hours or longer. Then, thearithmetic processing unit 45 inputs the data of the image predicted tobe imaged by the imaging device 20 after the elapse of the time Ta tothe convolutional neural network, and acquires information output fromthe convolutional neural network.

In addition, the calculation model for an image may include therecurrent neural network only. Such a recurrent neural network receivesan input of, for example, time-series image data imaged by the imagingdevice 20, and outputs information on a score indicating the degreewhether an environmental change indicating the above-described sign hasoccurred. Such a recurrent neural network is stored for each type ofsign in the storage unit 42. In the storage unit 42, controlinformation, which is information in which the type and the controltarget value of each device to be controlled are associated with eachother, is stored for each type of sign. Such control information can bestored in the storage unit 42 by the operator operating the input device63, for example.

The arithmetic processing unit 45 can input the time-series image dataimaged by the imaging device 20 to the recurrent neural network for eachtype of sign to acquire information on a score output from eachrecurrent neural network. The arithmetic processing unit 45 acquires,from the storage unit 42, control information including the type and thecontrol target value of the device to be controlled associated with thetype of sign of which score is equal to or higher than a threshold. Inaddition, in a case where there are multiple types of sign of whichscores are equal to or higher than the threshold, the arithmeticprocessing unit 45 acquires, from the storage unit 42, controlinformation including the type and the control target value of thedevice to be controlled associated with the type of sign of which scoreis highest. The arithmetic processing unit 45 outputs the acquiredcontrol information including the type and the control target value ofthe device to be controlled to the processing device 30 from thecommunication unit 41 as output information of the arithmetic device 40.

The learning processing unit 47 can generate and update theabove-described calculation model for an image on the basis of thelearning data output from the processing device 30. The learningprocessing unit 47 stores the generated or updated calculation model foran image in the storage unit 42.

For example, in a case where the calculation model for an image includesa convolutional neural network, the learning processing unit 47 cangenerate or update the calculation model for an image by optimizing theconvolutional neural network on the basis of the image data and theobject-to-be-controlled data included in the learning data.

In a case where the calculation model for an image includes therecurrent neural network, the learning processing unit 47 can generateor update the calculation model for an image by optimizing the recurrentneural network on the basis of the learning data including thetime-series image data.

The neural network in the arithmetic device 40 is an artificial neuralnetwork. The artificial neural network is a calculation model in whichperceptrons are hierarchically arranged, each of the perceptronsobtaining a weighted sum of input signals, applying a non-linearfunction called an activation function thereto, and outputting a resultof the application. The output out of the perceptron can be expressed bythe following equation (1), in which the input is expressed by X=(x1,x2, . . . , xn), the weight is expressed by W=(w1, w2, . . . , wn), theactivation function is expressed by f(.), and the element-wise productof vectors is expressed by *.

out=f(X*W)  (1)

In the convolutional neural network, perceptrons each receive atwo-dimensional signal corresponding to an image as an input, calculatea weighted sum of the inputs, and pass results of the calculation to thenext layer. As the activation function, a sigmoid function or arectified linear unit (ReLU) function is used.

The above-described perceptrons are hierarchically arranged in theartificial neural network, and an identification result is calculated byprocessing an input signal in each layer. In a final layer, for example,if the type of task in the artificial neural network is a regressiontask, an output of the activation function is used as it is as an outputof the task, and if the type of task is a classification task, a softmaxfunction is applied regarding the final layer, and a result of theapplication is used as an output of the task.

In a case of the convolutional neural network, an artificial network isconfigured as a map of two-dimensional signals. It can be consideredthat each of the two-dimensional signals corresponds to the perceptron.With respect to a feature map of the previous layer, the weighted sum iscalculated and the activation function is applied, and a result thereofis output.

The above-described process is called a convolution operation in theconvolutional neural network, and in addition thereto, a pooling layerfor performing a pooling process may be inserted in each layer. Thepooling layer performs downsampling by performing an averaging operationor a maximum operation on a feature map.

Learning by such an artificial neural network is performed by backpropagation, and for example, a known stochastic gradient descent methodis used. The back propagation is a framework in which an output error ofthe artificial neural network is propagated from the final layer toprevious layers in sequence to update weights.

Next, the control device 50 illustrated in FIG. 2 will be described. Thecontrol device 50 can control the water treatment device 10 bycontrolling the blower 14, the pump 15, and the like. For example, bycontrolling the blower 14 to adjust the amount of air blown into aliquid mixture containing activated sludge, the control device 50 cancontrol the concentration of dissolved oxygen in the liquid mixturecontaining activated sludge. In addition, by controlling the pump 15,the control device 50 adjusts the flow rate of activated sludge returnedto the treatment tank 12 from the final settling tank 13.

FIG. 7 is a diagram illustrating an example configuration of the controldevice according to the first embodiment. As illustrated in FIG. 7, thecontrol device 50 includes a communication unit 51, a storage unit 52, acontrol unit 53, and an input/output unit 54. The communication unit 51is connected to the communication network 64. The control unit 53 cantransmit and receive data to and from the processing device 30 via thecommunication unit 51 and the communication network 64.

The control unit 53 includes an input processing unit 55, a blowercontrol unit 56, and a pump control unit 57. The input processing unit55 acquires control information output from the processing device 30 viathe communication unit 51, and stores the acquired control informationin the storage unit 52. The control information stored in the storageunit 52 includes a control target value of the blower 14 and a controltarget value of the pump 15.

The blower control unit 56 reads the control target value of the blower14 stored in the storage unit 52. In addition, the blower control unit56 acquires numerical data indicating the amount of dissolved oxygendetected by the dissolved oxygen sensor 23 ₁ from the storage device 61or the dissolved oxygen sensor 23 ₁. The blower control unit 56generates a control signal by proportional integral (PI) control orproportional integral differential (PID) control on the basis of thecontrol target value of the blower 14 and the acquired amount ofdissolved oxygen. The blower control unit 56 outputs the generatedcontrol signal to the blower 14 from the input/output unit 54. Theblower 14 adjusts the amount of air blown into the treatment tank 12 onthe basis of the control signal output from the input/output unit 54 ofthe control device 50.

The pump control unit 57 reads the control target value of the pump 15stored in the storage unit 52. In addition, the pump control unit 57acquires, from a sensor (not illustrated) via the input/output unit 54,numerical data indicating the flow rate of the activated sludge to thetreatment tank 12 from the final settling tank 13. The pump control unit57 generates a control signal by PI control or PID control on the basisof the control target value of the pump 15 and the acquired flow rate ofthe activated sludge. The pump control unit 57 outputs the generatedcontrol signal to the pump 15 from the input/output unit 54. The pump 15adjusts the flow rate of the activated sludge to the treatment tank 12from the final settling tank 13 on the basis of the control signaloutput from the input/output unit 54 of the control device 50.

Next, an operation of the water treatment plant 1 will be described withreference to a flowchart. FIG. 8 is a flowchart illustrating an exampleof a series of processes of the processing device according to the firstembodiment, and the series of processes is repeatedly executed by thecontrol unit 33 of the processing device 30.

As illustrated in FIG. 8, the control unit 33 of the processing device30 determines whether a selection condition switching operation has beenaccepted from the operator (step S10). If it is determined that theselection condition switching operation has been accepted (step S10:Yes), the control unit 33 switches the selection condition by changingthe selection condition stored in the storage unit 32 to a selectioncondition depending on the switching operation (step S11).

When the process of step S11 ends, or if it is determined that theselection condition switching operation has not been accepted (step S10:No), the control unit 33 determines whether selection of image data hasbeen accepted from the operator (step S12). If it is determined that theselection of image data has been accepted (step S12: Yes), the controlunit 33 outputs learning data including the selected image data to thearithmetic device 40 (step S13).

When the process of step S13 ends, or if it is determined that theselection of the image data has not been accepted (step S12: No), thecontrol unit 33 determines whether the detection data has been acquired(step S14). If it is determined that the detection data has beenacquired (step S14: Yes), the control unit 33 determines whether theoperation mode is the automatic switching mode (step S15).

If it is determined that the operation mode is the automatic switchingmode (step S15: Yes), the control unit 33 performs an automaticswitching process (step S16). In step S16, when the control unit 33determines that the first switching condition is satisfied in a statewhere the calculation model for a sensor is set as the selectioncondition, the control unit 33 sets the calculation model for an imageas the selection condition. In addition, when the control unit 33determines that the second switching condition is satisfied in a statewhere the calculation model for an image is set as the selectioncondition, the control unit 33 sets the calculation model for a sensoras the selection condition.

When the process of step S16 ends, or if it is determined that theoperation mode is not the automatic switching mode (step S15: No), thecontrol unit 33 acquires detection data corresponding to the selectioncondition from the storage device 61, and outputs the acquired detectiondata to the arithmetic device 40 (step S17). In step S17, for example,in a case where the set selection condition is the calculation model foran image, the detection data corresponding to the selection condition isimage data. In addition, in a case where the set selection condition isthe calculation model for a sensor, the detection data corresponding tothe selection condition is numerical data.

Next, the control unit 33 acquires output information output from thearithmetic device 40 in response to step S17 (step S18), and outputs theacquired output information to the control device 50 (step S19). Suchoutput information includes the control information as described above.When the process of step S19 ends, or if it is determined that thedetection data has not been acquired (step S14: No), the control unit 33ends the processes illustrated in FIG. 8.

FIG. 9 is a flowchart illustrating an example of a series of processesof the arithmetic device according to the first embodiment, and theseries of processes is repeatedly executed by the control unit 43 of thearithmetic device 40.

As illustrated in FIG. 9, the control unit 43 of the arithmetic device40 determines whether the detection data has been acquired from theprocessing device 30 (step S20). If it is determined that the detectiondata has been acquired (step S20: Yes), the control unit 43 executes anarithmetic process using a calculation model and employing the acquireddetection data as an input of the calculation model (step S21), andtransmits output information of the calculation model to the processingdevice 30 (step S22).

When the process of step S22 ends, or if it is determined that thedetection data has not been acquired (step S20: No), the control unit 43determines whether the learning data has been acquired from theprocessing device 30 (step S23). If it is determined that the learningdata has been acquired from the processing device 30 (step S23: Yes),the control unit 43 executes a learning process of the calculation modelusing the learning data (step S24).

When the process of step S24 ends, or if it is determined that thelearning data has not been acquired (step S23: No), the control unit 43ends the processes illustrated in FIG. 9.

FIG. 10 is a flowchart illustrating an example of a series of processesof the control device according to the first embodiment, and the seriesof processes is repeatedly executed by the control unit 53 of thecontrol device 50.

As illustrated in FIG. 10, the control unit 53 of the control device 50determines whether the control information has been acquired from theprocessing device 30 (step S30). If it is determined that the controlinformation has been acquired (step S30: Yes), the control unit 53controls each device to be controlled on the basis of the acquiredcontrol information (step S31). When the process of step S31 ends, or ifit is determined that the control information has not been acquired(step S30: No), the control unit 53 ends the processes illustrated inFIG. 10.

FIG. 11 is a diagram illustrating an example of a hardware configurationof the processing device according to the first embodiment. Asillustrated in FIG. 11, the processing device 30 includes a computerincluding a processor 101, a memory 102, and an interface circuit 103.

The processor 101, the memory 102, and the interface circuit 103 cantransmit and receive data to and from each other via a bus 104. Thecommunication unit 31 is realized by the interface circuit 103. Thestorage unit 32 is realized by the memory 102. The processor 101executes functions of the data processing unit 34, the displayprocessing unit 35, the arithmetic-operation request unit 36, theacceptance processing unit 37, and the switching unit 38 by reading andexecuting programs stored in the memory 102. The processor 101 is anexample of a processing circuit, and includes one or more of a centralprocessing unit (CPU), a digital signal processor (DSP), and systemlarge scale integration (LSI).

The memory 102 includes one or more of a random access memory (RAM), aread only memory (ROM), a flash memory, and an erasable programmableread only memory (EPROM). In addition, the memory 102 includes arecording medium in which the above-described programs readable by thecomputer are recorded. Such a recording medium includes one or more of anon-volatile or volatile semiconductor memory, a magnetic disk, aflexible memory, an optical disk, a compact disc, and a DVD.

In a case where the control unit 33 of the processing device 30 isrealized by dedicated hardware, the control unit 33 is, for example, asingle circuit, a composite circuit, a programmed processor, a parallelprogrammed processor, an application specific integrated circuit (ASIC),a field programmable gate array (FPGA), or a combination thereof.

The arithmetic device 40 also includes a hardware configuration similarto the hardware configuration illustrated in FIG. 11. The communicationunit 41 is realized by the interface circuit 103. The storage unit 42 isrealized by the memory 102. The processor 101 executes functions of theacquisition processing unit 44, the arithmetic processing unit 45, theoutput processing unit 46, and the learning processing unit 47 byreading and executing the programs stored in the memory 102. In a casewhere the control unit 43 is realized by dedicated hardware, the controlunit 43 is a single circuit, a composite circuit, a programmedprocessor, a parallel programmed processor, an ASIC, an FPGA, or acombination thereof.

The control device 50 also includes a hardware configuration similar tothe hardware configuration illustrated in FIG. 11. The communicationunit 51 and the input/output unit 54 are realized by the interfacecircuit 103. The storage unit 52 is realized by the memory 102. Theprocessor 101 executes functions of the input processing unit 55, theblower control unit 56, and the pump control unit 57 by reading andexecuting programs stored in the memory 102. In a case where the controlunit 53 is realized by dedicated hardware, the control unit 53 is asingle circuit, a composite circuit, a programmed processor, a parallelprogrammed processor, an ASIC, an FPGA, or a combination thereof.

In the example described above, the information output from thearithmetic device 40 is output to the control device 50 from theprocessing device 30, but a configuration may be employed in which theinformation output from the arithmetic device 40 is directly input tothe control device 50 without the processing device 30.

In a case where the calculation model for an image includes therecurrent neural network, it is possible to output, to the processingdevice 30 for each type of sign, information on a sign score which is ascore indicating the degree whether there is the sign of the futureunfavorable intra-tank state in the water treatment device 10. In such acase, the display processing unit 35 of the processing device 30 candisplay the acquired sign score for each type of sign on the displaydevice 62.

In the above-described example, the calculation model which employsimage data only as input data has been described as an example of thecalculation model for an image, but the calculation model for an imagemay be a calculation model which employs, in addition to image data,numerical data or other data as input data.

Although the convolutional neural network which receives inputs ofmultiple image data and outputs multiple control target values has beendescribed above as an example of the calculation model for an image, thecalculation model for an image is not limited to the example describedabove. For example, the convolutional neural network can be provided foreach control target value as the calculation model for an image. Theconvolutional neural network can be provided for each imaging device 20as the calculation model for an image. In addition, the convolutionalneural network can be provided for each imaging device 20 and eachdevice to be controlled as the calculation model for an image.

In the above-described example, in a case where the calculation modelfor an image includes the recurrent neural network only, the controlinformation, which is information in which the type and the controltarget value of each device to be controlled are associated with eachother, is stored for each type of sign, but there is not limitation tosuch an example. For example, the arithmetic device 40 can also generateor update the recurrent neural network by performing machine learning onthe basis of the time-series image data and the time-series controltarget values stored in the storage device 61. In such a case, therecurrent neural network outputs the control target values from thetime-series image data. As a result, effective water treatment can beperformed, for example, even in a case where there is a sign in thewater treatment plant 1, the sign being one of multiple signs of thefuture unfavorable intra-tank state and being not yet recognized by theoperator.

In the above-described example, the blower 14 and the pump 15 have beendescribed as examples of the device to be controlled which is controlledby using the arithmetic device 40, but the device to be controlled whichis controlled by using the arithmetic device 40 may include devicesother than the blower 14 and the pump 15.

As described above, the water treatment plant 1 according to the firstembodiment includes the water treatment device 10 which performs watertreatment, the imaging device 20, the processing device 30, thearithmetic device 40, and the control device 50. The imaging device 20images a water treatment environment of the water treatment device 10and outputs image data obtained by imaging. The processing device 30causes the arithmetic device 40 which performs an arithmetic operationusing one or more calculation models generated by machine learning toexecute the arithmetic operation employing the image data output fromthe imaging device 20 as input data of the one or more calculationmodels. The control device 50 controls the water treatment device 10 onthe basis of output information output from the arithmetic device 40 byexecuting the arithmetic operation. Therefore, in the water treatmentplant 1, it is possible to perform, with the use of the arithmeticdevice 40, for example, water treatment control which has been performedby the operator of the water treatment plant 1 on the basis of an imageof the water treatment environment of the water treatment device 10 andon the basis of past experience or knowledge of the operator. Therefore,more effective water treatment control can be performed with respect toa change in the water treatment environment.

In addition, the one or more calculation models include a convolutionalneural network employing image data as input data. The processing device30 causes the arithmetic device 40 to execute an arithmetic operationusing the convolutional neural network. The convolutional neural networkis an example of the calculation model for an image. As described above,by preparing the convolutional neural network employing image data asinput data and causing the arithmetic device 40 to execute thearithmetic operation using the convolutional neural network on the imagedata output from the imaging device 20, the water treatment device 10can be accurately controlled.

The water treatment plant 1 includes a sensor which detects acharacteristic indicating the water treatment environment of the watertreatment device 10 and outputs numerical data of the detectedcharacteristic. The arithmetic device 40 includes a neural network for asensor which employs numerical data output from the sensor as inputdata. The neural network for a sensor is an example of the calculationmodel for a sensor described above. The processing device 30 causes thearithmetic device 40 to execute an arithmetic operation using the neuralnetwork for a sensor. As described above, by detecting thecharacteristic indicating the water treatment environment of the watertreatment device 10 with a sensor 2, outputting numerical data of thedetected characteristic from the sensor 2, preparing the neural networkfor a sensor which employs the numerical data output from the sensor 2as input data, and causing the arithmetic device 40 to execute thearithmetic operation using the neural network for a sensor on thenumerical data output from the sensor 2, it is possible to control thewater treatment device 10 using a detection result of the sensor.

The processing device 30 includes the switching unit 38 which performsswitching between the use of the convolutional neural network and theuse of the neural network for a sensor to cause the arithmetic device 40to execute the arithmetic operation. As a result, the water treatmentdevice 10 can be accurately controlled, for example, by performingswitching between the water treatment control using the image imaged bythe imaging device 20 and the water treatment control using thedetection result by the sensor depending on the situation.

In addition, the processing device 30 includes the acceptance processingunit 37 which accepts selection of one or more image data from multipleimage data imaged by the imaging device 20. The arithmetic device 40executes machine learning of one or more calculation models on the basisof the one or more image data accepted by the acceptance processing unit37. As a result, for example, the calculation models included in thearithmetic device 40 can be updated, and the water treatment device 10can be accurately controlled.

The control device 50 controls each device to be controlled provided inthe water treatment device 10 by proportional-integral control orproportional-integral-derivative control. As a result, the watertreatment device 10 can be accurately controlled.

The water treatment device 10 includes the devices to be controlledwhich are objects to be controlled by the control device 50. Theprocessing device 30 causes the arithmetic device 40 to execute anarithmetic operation to generate control target values RV1 and RV2 ofthe devices to be controlled. The control device 50 controls the watertreatment device 10 employing the control target values RV1 and RV2caused to be generated by the processing device 30 as outputinformation. As a result, the devices to be controlled provided in thewater treatment device 10 can be accurately controlled.

The configurations described in the embodiment above are merely examplesof the content of the present invention and can be combined with otherknown technology and part thereof can be omitted or modified withoutdeparting from the gist of the present invention.

REFERENCE SIGNS LIST

1 water treatment plant; 10 water treatment device; 11 primary settlingtank; 12 treatment tank; 13 final settling tank; 14 blower; 15 pump; 20,20 ₁, 20 ₂, 20 ₃ imaging device; 21, 21 ₁, 21 ₂, 21 ₃ sensor group; 22 ₁flow rate sensor; 22 ₂ BOD sensor; 22 ₃ water temperature sensor; 22 ₄NH₃ sensor; 23 ₁ dissolved oxygen sensor; 23 ₂ active microorganismconcentration sensor; 23 ₃ BOD sensor; 24 ₁ flow rate sensor; 24 ₂ BODsensor; 24 ₃ total nitrogen concentration sensor; 30 processing device;31, 41, 51 communication unit; 32, 42, 52 storage unit; 33, 43, 53control unit; 34 data processing unit; 35 display processing unit; 36arithmetic-operation request unit; 37 acceptance processing unit; 38switching unit; 40 arithmetic device; 44 acquisition processing unit; 45arithmetic processing unit; 46 output processing unit; 47 learningprocessing unit; 50 control device; 54 input/output unit; 55 inputprocessing unit; 56 blower control unit; 57 pump control unit; 61storage device; 62 display device; 63 input device; 64 communicationnetwork.

1.-3. (canceled)
 4. A water treatment plant that performs watertreatment using a water treatment device, the water treatment plantcomprising: a monitor to image a water treatment environment of thewater treatment device and to output image data obtained by imaging; asensor to detect a characteristic that indicates a water treatmentenvironment of the water treatment device and to output numerical dataof the detected characteristic; a processing circuitry to cause anarithmetic circuitry that performs an arithmetic operation using one ormore calculation models including a convolutional neural network and aneural network for a sensor that is different from the convolutionalneural network to execute the arithmetic operation employing the imagedata output from the imaging device as input data of the convolutionalneural network and to execute the arithmetic operation employing thenumerical data output from the sensor as input data of the neuralnetwork for a sensor; and a control circuitry to control the watertreatment device on a basis of output information output from thearithmetic circuitry by executing the arithmetic operation, wherein theprocessing circuitry includes a switcher to perform switching betweenuse of the convolutional neural network and use of the neural networkfor a sensor to cause the arithmetic circuitry to execute the arithmeticoperation.
 5. The water treatment plant according to claim 4, whereinthe processing circuitry includes an acceptance processing circuitry toaccept selection of one or more image data among a plurality of imagedata imaged by the imaging device, and the arithmetic circuitry executesmachine learning of the one or more calculation models on a basis of theone or more image data accepted by the acceptance processing circuitry.6. The water treatment plant according to claim 4, wherein the controlcircuitry controls a device to be controlled provided in the watertreatment device by proportional-integral control orproportional-integral-derivative control.
 7. The water treatment plantaccording to claim 4, wherein the arithmetic circuitry is AI.
 8. Thewater treatment plant according to claim 4, wherein the water treatmentdevice includes a device to be controlled that is an object to becontrolled by the control circuitry, the processing circuitry causes thearithmetic circuitry to execute the arithmetic operation to generate acontrol target value of the device to be controlled, and the controlcircuitry controls the water treatment device using the control targetvalue caused to be generated by the processing circuitry as the outputinformation. 9.-11. (canceled)
 12. A method for operating a watertreatment plant that performs water treatment using a water treatmentdevice, the method comprising: imaging a water treatment environment ofthe water treatment device and outputting image data obtained byimaging; detecting a characteristic that indicates a water treatmentenvironment of the water treatment device by a sensor and outputtingnumerical data of the detected characteristic; causing an arithmeticcircuitry that performs an arithmetic operation using one or morecalculation models including a convolutional neural network and a neuralnetwork for a sensor that is different from the convolutional neuralnetwork to execute the arithmetic operation employing the image dataoutput as input data of the convolutional neural network and to executethe arithmetic operation employing the numerical data output from thesensor as input data of the neural network for a sensor; controlling thewater treatment device on a basis of output information output from thearithmetic circuitry by executing the arithmetic operation; andperforming switching between the convolutional neural network and theneural network for a sensor used by the arithmetic circuitry to causethe arithmetic circuitry to execute the arithmetic operation.
 13. Themethod for operating a water treatment plant according to claim 12,comprising: accepting selection of one or more image data from aplurality of the image data imaged; and executing machine learning ofthe one or more calculation models on a basis of the one or more imagedata selected.
 14. The method for operating a water treatment plantaccording to claim 12, wherein in controlling the water treatmentdevice, a device to be controlled provided in the water treatment deviceis controlled by proportional-integral control orproportional-integral-derivative control.
 15. The method for operating awater treatment plant according to claim 12, comprising: preparing AI asthe arithmetic circuitry.
 16. The method for operating a water treatmentplant according to claim 12, wherein the water treatment device includesa device to be controlled that is an object to be controlled, and themethod comprises: causing a control target value of the device to becontrolled to be generated as output information output from thearithmetic circuitry by executing the arithmetic operation; andcontrolling the water treatment device employing the control targetvalue generated as the output information.